Fetching the paper…
Reading the bibliography…
Recent studies in Retrieval-Augmented Generation (RAG) have investigated extracting evidence from retrieved passages to reduce computational costs and enhance the final RAG performance, yet it remains challenging.
Knowledge guided text retrieval and reading for open domain question answering
Sewon Min, Danqi Chen, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 1911
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan T. McDonald. 2020 · 1919
Earlier work this paper cites.
N -gram similarity and distance
Grzegorz Kondrak. 2005 · 2005
Earlier work this paper cites.
Learning to rank with nonsmooth cost functions
Christopher J. C. Burges, Robert Ragno, and Quoc Viet Le. 2006 · 2006
Earlier work this paper cites.
On the local optimality of lambdarank
Pinar Donmez, Krysta M. Svore, and Christopher J. C. Burges. 2009 · 2009
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
The lambdaloss framework for ranking metric optimization
Xuanhui Wang, Cheng Li, Nadav Golbandi, Michael Bendersky, and Marc Najork. 2018 · 2018
Earlier work this paper cites.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Earlier work this paper cites.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Answering naturally: Factoid to full length answer generation
Vaishali Pal, Manish Shrivastava, and Irshad Bhat. 2019 · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick S. H. Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
Multi-loss weighting with coefficient of variations
Rick Groenendijk, Sezer Karaoglu, Theo Gevers, and Thomas Mensink. 2021 · 2021
Earlier work this paper cites.
Natural answer generation: From factoid answer to full-length answer using grammar correction
Manas Jain, Sriparna Saha, Pushpak Bhattacharyya, Gladvin Chinnadurai, and Manish Kumar Vatsa. 2021 · 2021
Cited alongside, same era.
Increasing faithfulness in knowledge-grounded dialogue with controllable features
Hannah Rashkin, David Reitter, Gaurav Singh Tomar, and Dipanjan Das. 2021 · 2021
Cited alongside, same era.
Constitutional AI: harmlessness from AI feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosiute, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemí Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan. 2022 · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2023 · 2023
Later among the works it cites.
Retrieval-augmented domain adaptation of language models
Benfeng Xu, Chunxu Zhao, Wenbin Jiang, Pengfei Zhu, Songtai Dai, Chao Pang, Zhuo Sun, Shuohuan Wang, and Yu Sun. 2023 · 2023
Later among the works it cites.
Alignscore: Evaluating factual consistency with A unified alignment function
Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu. 2023 · 2023
Later among the works it cites.
Retrieve anything to augment large language models
Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, and Jian-Yun Nie. 2023 · 2023
Later among the works it cites.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2024 · 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Evaluating correctness and faithfulness of instruction-following models for question answering
Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy. 2023 · 2023
Cited alongside, same era.
Complex claim verification with evidence retrieved in the wild
Jifan Chen, Grace Kim, Aniruddh Sriram, Greg Durrett, and Eunsol Choi. 2023 · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang. 2023 · 2023
Cited alongside, same era.
Active retrieval augmented generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023b · 2023
Cited alongside, same era.
Md. Tahmid Rahman Laskar, Mizanur Rahman, Israt Jahan, Enamul Hoque, and Jimmy Huang. 2023 · 2023
Cited alongside, same era.
Compressing context to enhance inference efficiency of large language models
Yucheng Li, Bo Dong, Frank Guerin, and Chenghua Lin. 2023b · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D. Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Cited alongside, same era.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Cited alongside, same era.
Closest in time.
A survey on rag meets llms: Towards retrieval-augmented large language models
Yujuan Ding, Wenqi Fan, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. 2024 · 2024
Closest in time.
Jiajie Jin, Yutao Zhu, Yujia Zhou, and Zhicheng Dou. 2024 · 2024
Closest in time.
Sure: Summarizing retrievals using answer candidates for open-domain QA of llms
Jaehyung Kim, Jaehyun Nam, Sangwoo Mo, Jongjin Park, Sang-Woo Lee, Minjoon Seo, Jung-Woo Ha, and Jinwoo Shin. 2024 · 2024
Closest in time.
Evidence-focused fact summarization for knowledge-augmented zero-shot question answering
SungHo Ko, Hyunjin Cho, Hyungjoo Chae, Jinyoung Yeo, and Dongha Lee. 2024 · 2024
Closest in time.
Learning to trust your feelings: Leveraging self-awareness in llms for hallucination mitigation
Yuxin Liang, Zhuoyang Song, Hao Wang, and Jiaxing Zhang. 2024 · 2024
Closest in time.
Lipo: Listwise preference optimization through learning-to-rank
Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, Peter J. Liu, and Xuanhui Wang. 2024 · 2024
Closest in time.
Interpretable long-form legal question answering with retrieval-augmented large language models
Antoine Louis, Gijs van Dijck, and Gerasimos Spanakis. 2024 · 2024
Closest in time.
RAPTOR: recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D. Manning. 2024 · 2024
Closest in time.
Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. 2024 · 2024
Closest in time.
Almanac—retrieval-augmented language models for clinical medicine
Cyril Zakka, Rohan Shad, Akash Chaurasia, Alex R Dalal, Jennifer L Kim, Michael Moor, Robyn Fong, Curran Phillips, Kevin Alexander, Euan Ashley, et al. 2024 · 2024
Closest in time.
Self-alignment for factuality: Mitigating hallucinations in llms via self-evaluation
Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, and Helen Meng. 2024 · 2024
Closest in time.
Llamafactory: Unified efficient fine-tuning of 100+ language models
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, and Yongqiang Ma. 2024 · 2024
Closest in time.